{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SweINiRd8dLd"
      },
      "source": [
        "# 使用Yi的API构建RAG (使用LlamaIndex)\n",
        "\n",
        "`LlamaIndex`是一个灵活的框架，用于构建基于LLM的应用程序，提供数据连接器、数据结构化工具和高级检索/查询接口。\n",
        "\n",
        "\n",
        "在本次示例中我们使用`bge-base-zh-v1.5`进行演示，这个模型主要用于检索中文。你也可以选择`bge-base-en-v1.5`模型来检索英文文档。\n"
      ],
      "id": "SweINiRd8dLd"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6989tVXX8dLh",
        "outputId": "ccdd7497-7132-4eeb-fe89-6cab8d3d9e9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: minijinja, sentence-transformers, llama-index-embeddings-huggingface\n",
            "Successfully installed llama-index-embeddings-huggingface-0.2.2 minijinja-2.0.1 sentence-transformers-3.0.1\n"
          ]
        }
      ],
      "source": [
        "# 安装所需的Python包\n",
        "!pip install llama-index\n",
        "!pip install llama-index-llms-huggingface\n",
        "!pip install llama-index-readers-web\n",
        "!pip install llama-index-llms-yi\n",
        "!pip install llama-index-embeddings-huggingface\n"
      ],
      "id": "6989tVXX8dLh"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlxCBbmI8dLi"
      },
      "source": [
        "### 初始化Yi的API\n",
        "\n",
        "前往[零一万物开放平台](https://platform.lingyiwanwu.com/)，注册并获取免费的API额度来体验yi-large API。\n",
        "然后点击[这里](https://platform.lingyiwanwu.com/apikeys)，申请你的API密钥（api_key）。\n",
        "\n",
        "导入依赖包，并初始化api平台，你只需要在`your_api_key`处替换成你申请的api_key即可。\n"
      ],
      "id": "XlxCBbmI8dLi"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
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          "height": 496,
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        "id": "dY6eqq4u8dLi",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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        }
      ],
      "source": [
        "from llama_index.llms.yi import Yi\n",
        "from llama_index.readers.web import SimpleWebPageReader\n",
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings\n",
        "\n",
        "# 初始化API\n",
        "llm = Yi(model=\"yi-large\", api_key=\"your-api-key\")\n",
        "Settings.llm = llm\n",
        "Settings.embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-base-zh-v1.5\")"
      ],
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    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "### 构建知识库\n",
        "\n",
        "在这里我们构建一个本地知识库。首先，加载需要索引的文档，然后使用`VectorStoreIndex`进行索引。\n"
      ],
      "id": "AQWrtgZk8dLj"
    },
    {
      "cell_type": "markdown",
      "source": [
        "这里请注意啦，data你可以在同级文件创建一个文件夹然后把数据放在里面就行啦!"
      ],
      "metadata": {
        "id": "RORbGmmc922u"
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      "id": "RORbGmmc922u"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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        "# 加载本地文档并构建索引\n",
        "documents = SimpleDirectoryReader(\"data\").load_data()\n",
        "index = VectorStoreIndex.from_documents(documents)\n",
        "query_engine = index.as_query_engine()"
      ],
      "id": "tLuQt9bf8dLj"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwZK0WGY8dLj"
      },
      "source": [
        "### 进行对话交流\n",
        "\n",
        "现在，我们可以进行对话交流了。`yi-large`模型会基于检索到的文档生成答案。\n"
      ],
      "id": "UwZK0WGY8dLj"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "9hxFwnOJ8dLk"
      },
      "outputs": [],
      "source": [
        "# 开始对话\n",
        "while True:\n",
        "    user_input = input(\"user>> \")\n",
        "    response = query_engine.query(user_input)\n",
        "    print('Yi-large>>', response)"
      ],
      "id": "9hxFwnOJ8dLk"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpqIG-_A8dLk"
      },
      "source": [
        "哈哈现在你已经完成了使用Yi的API构建RAG的基本步骤。你可以进一步探索和思考如何根据你自己的具体需求调整和优化你的RAG系统。"
      ],
      "id": "SpqIG-_A8dLk"
    }
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